AI Learning Hub

Your Complete AI Roadmap

From Zero to Real-World Projects

Hi! I’m Dan, a Machine Learning Engineer, and I’m on a journey to build the ultimate AI Roadmap, designed to help you learn how to create, deploy, and scale machine learning systems from basics to advanced levels.

Join the Program

In this continuous cohort model, you can learn at your own pace or join the community to learn together as I create and release new content. Each month, fresh topics and projects are added, so there’s always something exciting to explore. You’ll have the flexibility to move through the curriculum however you prefer, while still having access to the support of a vibrant community of AI learners.

Whether you’re looking to master the latest deep learning techniques, deploy machine learning models, or scale AI systems, you’ll be part of the journey, growing together with the community as the AI Learning Hub evolves.

What will you get?

✔ 10+ hours of AI content from the fundamentals to advanced.

✔ Hands-on machine learning and deep learning projects with step-by-step coding instructions.

✔ Real-world end-to-end projects to help you build a professional AI portfolio.

✔ A private collaborative community of AI learners and professionals.

✔ Receive feedback on your projects from peers and community members.

✔ Direct access to your instructor.

✔ Lifetime access to every past and future courses and content.

30-Day Free Trial – No Risk, No Problem!

Join today and enjoy a full 30-day free trial with complete access to all content. No strings attached – experience the program and decide if it's right for you. If you're not satisfied, you can cancel at any time during the trial with zero cost. We’re confident you’ll love it, but you’ve got nothing to lose with our risk-free guarantee!

Monthly

Subscribe monthly for access and join as many iterations as you want, with the freedom to cancel anytime.

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Lifetime

Pay once to join the program and get lifetime access. You’re free to take part in as many iterations as you wish, with no limitations.

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Program Syllabus

The AI Learning Hub is your ongoing path to mastering AI. This syllabus outlines the key topics you’ll cover throughout the program. Each section is designed to build on the last, ensuring you develop both foundational and advanced skills through practical, hands-on learning. As part of this continuous cohort, new content will be added regularly, so you’ll always be learning the latest in AI.

Introduction to Machine Learning: Learn the fundamentals and applications of machine learning.
Types of Machine Learning: Understand Supervised, Unsupervised, and Reinforcement Learning.
Key Concepts: Explore features, labels, and datasets in machine learning.
Data Preprocessing: Clean, normalize, and encode data for machine learning models.
Train-Test Split and Cross-Validation: Split datasets and validate models effectively.
Evaluation Metrics: Measure model performance with metrics like Accuracy, Precision, Recall, F1-Score, and RMSE.
Linear Regression and Polynomial Regression: Predict continuous values using regression models.
Logistic Regression: Apply logistic regression for classification tasks.
Decision Trees and Random Forests: Build interpretable models for classification and regression.
Support Vector Machines (SVMs): Learn to classify data using hyperplanes and kernels.
K-Nearest Neighbors (KNN): Understand instance-based learning for classification and regression.
Naive Bayes Classifiers: Explore probabilistic models for classification tasks.
Ensemble Methods: Improve performance with Bagging, Boosting, and Stacking techniques.
Gradient Boosting: Learn advanced techniques like XGBoost, LightGBM, and CatBoost.
Clustering Algorithms: Discover unsupervised learning methods like K-Means, DBSCAN, and Hierarchical Clustering.
Dimensionality Reduction: Reduce feature space using PCA, t-SNE, and UMAP.
Handling Imbalanced Datasets: Use techniques like SMOTE and undersampling to balance datasets.
Feature Engineering and Selection: Improve model performance by engineering and selecting relevant features.
Regularization Techniques: Prevent overfitting with L1, L2, and Elastic Net regularization.
Hyperparameter Tuning: Optimize models with Grid Search, Random Search, and Bayesian Optimization.
Building and Evaluating ML Pipelines: Automate and streamline the machine learning workflow.

Machine Learning Projects:

Building a Movie Recommendation Engine with Naive Bayes: Develop a system that suggests movies to users based on their preferences using the Naive Bayes algorithm.
Recognizing Faces with Support Vector Machine: Implement a facial recognition system utilizing Support Vector Machines (SVM) to identify individuals from images.
Predicting Online Ad Click-Through with Tree-Based Algorithms: Create models to predict the likelihood of users clicking on online advertisements using decision trees and ensemble methods.
Predicting Online Ad Click-Through with Logistic Regression: Apply logistic regression techniques to forecast user interactions with online ads.
Scaling Up Prediction to Terabyte Click Logs: Learn strategies to handle and analyze large-scale datasets, focusing on click logs, to make accurate predictions.
Predicting Stock Prices with Regression Algorithms: Use regression models to predict stock market prices based on historical data.
Mining the 20 Newsgroups Dataset with Text Analysis Techniques: Perform text mining and natural language processing on the 20 Newsgroups dataset to classify documents.
Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling: Utilize clustering algorithms and topic modeling to uncover hidden themes within the Newsgroups dataset.
Machine Learning Best Practices: Gain insights into best practices for developing, evaluating, and deploying machine learning models effectively.

Phase 6: Deep Learning Fundamentals (with Projects)

Topics Covered:

Basics of Neural Networks: Understand the foundational concepts of neural networks.
Neurons and Perceptrons: Learn how individual units in neural networks process data.
Layers in Neural Networks: Explore the roles of input, hidden, and output layers.
Activation Functions: Study functions like ReLU, Sigmoid, and Tanh and their impact on network behavior.
Weights, Biases, and their Roles: Understand how weights and biases influence predictions.
Forward Propagation: Calculate outputs in neural networks using forward propagation.
Loss Functions: Explore loss functions such as Mean Squared Error and Cross-Entropy.
Backpropagation and Gradient Descent: Learn how neural networks adjust weights to minimize loss.
Understanding Optimizers: Study optimization techniques like SGD, Adam, and their variants.
Overfitting and Regularization: Discover techniques like Dropout and L2 Regularization to prevent overfitting.
Batch Normalization: Improve training speed and performance with batch normalization.
Building Neural Networks from Scratch: Create simple networks using Python and Numpy.
Introduction to TensorFlow or PyTorch: Learn to implement neural networks with popular deep learning frameworks.
Tuning Hyperparameters: Optimize learning rate, batch size, and epochs for better performance.
Visualizing Neural Network Behavior: Analyze loss curves and accuracy trends during training.
Convolutional Neural Networks (CNNs): Understand and apply CNNs for image processing tasks.
Recurrent Neural Networks (RNNs): Work with sequential data for tasks like time series or text analysis.
Long Short-Term Memory Networks (LSTMs): Handle long dependencies in sequence data using LSTMs.
Gated Recurrent Units (GRUs): Explore simplified LSTM variants for sequence modeling.
Autoencoders: Apply autoencoders for dimensionality reduction and feature learning.
Generative Adversarial Networks (GANs): Generate synthetic images and data using GANs.

Deep Learning Projects: (more will be added in the future)

Predicting Stock Prices with Artificial Neural Networks: Implement neural network architectures to forecast stock prices, exploring deep learning approaches.
Categorizing Images of Clothing with Convolutional Neural Networks: Build and train convolutional neural networks (CNNs) to classify images of clothing items.
Making Predictions with Sequences Using Recurrent Neural Networks: Explore recurrent neural networks (RNNs) for sequence prediction tasks, such as time series forecasting.

Phase 7: Model Deployment & MLOps

Topics Covered:

Model Deployment Strategies: Learn how to deploy models using Flask, FastAPI, and cloud platforms.
Docker & Kubernetes: Containerize your applications and deploy them at scale.
Kubeflow: Set up workflows for automating ML pipelines.
MLflow: Track experiments and manage the machine learning lifecycle.
Airflow: Manage data workflows and model pipelines.
Cloud-Based Deployment: Deploy your models on platforms like AWS, GCP, and Azure.
Monitoring & Logging: Use tools like Prometheus and Grafana to monitor model performance and ensure they remain accurate over time.
CI/CD: Automate the deployment of machine learning models using CI/CD pipelines.

Phase 8: End-to-End Machine Learning Projects

Topics Covered:

Complete ML Pipelines: Learn how to build a fully functional machine learning pipeline from data collection to deployment.
Data Preprocessing: Clean, process, and prepare data for machine learning models.
Model Building & Training: Implement and train machine learning models tailored to real-world scenarios.
Model Deployment: Deploy machine learning models into production environments, integrating with APIs and cloud services.
Monitoring & Maintenance: Understand how to monitor model performance over time and retrain models as needed.

Advanced and Custom Topics

Topics Covered:

Advanced NLP & Transformers: Dive deep into cutting-edge natural language processing techniques and transformer architectures.
Generative AI Models: Explore AI models that generate text, images, and audio, including GANs and diffusion models.
Custom AI Solutions: Learn how to customize AI models for specialized tasks and industries.
Suggest a Topic: You can suggest any advanced topics or areas of interest, and we will explore them together as part of the curriculum.

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